Systems and methods for modeling a manufacturing assembly line

ABSTRACT

Various systems and methods for modeling a manufacturing assembly line are disclosed herein. Some embodiments relate to operating a processor to receive cell data, extract feature data from the cell data, determine a plurality of cell configurations, determine an efficiency score by applying the feature data to a predictive model generated for predicting a production level of the manufacturing assembly line, determine at least one target cell configuration from the cell configurations based on the efficiency score, and apply the at least one target cell configuration to at least one cell by implementing each target cell configuration to a corresponding cell.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 63/002,916 filed Mar. 31, 2020 and titled “SYSTEM AND METHODS FORMODELING A MANUFACTURING ASSEMBLY LINE”, the contents of which areincorporated herein by reference for all purposes.

FIELD

The described embodiments relate to systems and methods for modelingmanufacturing assembly lines, and to systems and methods for applyingthe predictive model to the manufacturing assembly line in variousapplications.

BACKGROUND

Manufacturing processes can involve processing (e.g., assembling,fabricating, treating, refining, etc.) raw materials or parts to produceproducts. During a manufacturing process, a partially-finished productmay be referred to as a workpiece. Manufacturing processes can involveassembly lines in which a workpiece is successively processed to producea final product. The workpiece can be moved through the assembly line tovarious machines that sequentially perform various processing on theworkpiece. Modern manufacturing assembly lines can involve a largenumber of highly configurable machines that can produce complex finalproducts. As a result, it can be difficult to monitor the operation of amanufacturing assembly line, and make any adjustments and/or repairswithout affecting the overall production of the manufacturing assemblyline.

SUMMARY

The various embodiments described herein generally relate to systems andmethods for modeling a manufacturing assembly line. The disclosedsystems and methods may relate to training models for predicting one ormore properties of the manufacturing assembly line, such as a productionlevel. The disclosed systems and methods may also relate to optimizingthe configuration of a manufacturing assembly line or evaluating faultsin a manufacturing assembly line.

In accordance with some embodiments, there is provided a method forgenerating a predictive model for predicting a production level of amanufacturing assembly line. The manufacturing assembly line includes aplurality of cells. Each cell is configured to successively process aworkpiece along the manufacturing assembly line. The method involvesoperating a processor to: receive cell data associated with at least onecell during an operation of an active manufacturing assembly line, thecell data including, for each cell, at least one input state of thatcell and a cell position of that cell within the active manufacturingassembly line; receive line production data associated with the celldata, the line production data being representative of a productionlevel of the active manufacturing assembly line in association with therespective cell data; determine one or more production associationsbetween the cell data of each cell and the production level of theactive manufacturing assembly line; evaluate the one or more productionassociations to identify one or more critical production associations tothe operation of the active manufacturing assembly line; retrieve thecell data and the line production data associated with the one or morecritical production associations; and train the predictive model withthe retrieved cell data and the retrieved line production data topredict the production level of the manufacturing assembly line.

In some embodiments, the line production data may be representative of adefect level of the active manufacturing assembly line, the one or moreproduction associations may include one or more associations between thecell data of each cell and the defect level of the active manufacturingassembly line, and the predictive model may be trained to predict thedefect level of the manufacturing assembly line.

In some embodiments, the one or more production associations may includea cell input association between an input state of a cell and theproduction level of the active manufacturing assembly line, and a cellposition association between a cell position of a cell and theproduction level of the active manufacturing assembly line.

In some embodiments, evaluating the one or more production associationsmay involve identifying one or more one statistically significantproduction associations as the one or more critical productionassociations.

In some embodiments, identifying the one or more statisticallysignificant production associations may involve: determining aprobability value of each production association in the plurality ofproduction associations being unassociated; and identifying a productionassociation as the one of the one or more statistically significantassociations if the probability value of that production association isless than or equal to a predetermined significance level.

In some embodiments, the cell data may include a cell position of afirst cell relative to a second cell in the active manufacturingassembly line. The plurality of production associations may include acell position association between the cell position of the first cellrelative to the second cell and the production level of the activemanufacturing assembly line.

In some embodiments, the first cell may be downstream of the second cellin the active manufacturing assembly line.

In some embodiments, the first cell may be upstream of the second cellin the active manufacturing assembly line.

In some embodiments, at least some of the cell data may correspond to afirst time. At least some of the line production data may correspond toa second time later than the first time. The one or more productionassociations may include a production association between cell datacorresponding to the first time and a production level corresponding tothe second time.

In some embodiments, evaluating the one or more production associationsmay involve: identifying the production association between the celldata corresponding to the first time and the production levelcorresponding to the second time as one of the one or more criticalproduction associations if a difference between the first time and thesecond time is less than a pre-determined time limit.

In some embodiments, the at least one input state may include a starvedstate in which the corresponding cell received an undersupply of atleast one input for processing the workpiece.

In some embodiments, the predictive model may include a decision tree.The decision tree may include a plurality of nodes. Training thepredictive model may involve generating at least one node correspondingto the one or more critical production associations.

In some embodiments, each cell may include at least one deviceconfigured to process the workpiece. The method may further involveoperating the processor to receive device data associated with at leastone device during the operation of the active manufacturing assemblyline. The device data may include, for each device, at least one devicestate. The plurality of production associations may include anassociation between a device state and the production level of theactive manufacturing assembly line.

In accordance with some embodiments, there is provided a non-transitorycomputer-readable medium including instructions executable on aprocessor for implementing the method.

In accordance with some embodiments, there is provided a system forgenerating a predictive model for predicting a production level of amanufacturing assembly line. The manufacturing assembly line includes aplurality of cells. Each cell is configured to successively process aworkpiece along the manufacturing assembly line. The system includes aprocessor configured to: receive cell data associated with at least onecell during an operation of an active manufacturing assembly line, thecell data including, for each cell, at least one input state of thatcell and a cell position of that cell within the active manufacturingassembly line; receive line production data associated with the celldata, the line production data being representative of a productionlevel of the active manufacturing assembly line in association with therespective cell data; determine one or more production associationsbetween the cell data of each cell and the production level of theactive manufacturing assembly line; evaluate the one or more productionassociations to identify one or more critical production associations tothe operation of the active manufacturing assembly line; retrieve thecell data and the line production data associated with the one or morecritical production associations; and train the predictive model withthe retrieved cell data and the retrieved line production data topredict the production level of the manufacturing assembly line.

In some embodiments, the line production data may be representative of adefect level of the active manufacturing assembly line, the one or moreproduction associations may include one or more associations between thecell data of each cell and the defect level of the active manufacturingassembly line, and the predictive model may be trained to predict thedefect level of the manufacturing assembly line.

In some embodiments, the one or more production associations may includea cell input association between an input state of a cell and theproduction level of the active manufacturing assembly line, and a cellposition association between a cell position of a cell and theproduction level of the active manufacturing assembly line.

In some embodiments, the processor may be configured to identify one ormore statistically significant production associations as the one ormore critical production associations.

In some embodiments, the processor may be further configured to:determine a probability value of each production association in theplurality of production associations being unassociated; and identify aproduction association as the one of the one or more statisticallysignificant associations if the probability value of that productionassociation is less than or equal to a predetermined significance level.

In some embodiments, the cell data may include a cell position of afirst cell relative to a second cell in the active manufacturingassembly line. The plurality of production associations may include acell position association between the cell position of the first cellrelative to the second cell and the production level of the activemanufacturing assembly line.

In some embodiments, the first cell may be downstream of the second cellin the active manufacturing assembly line.

In some embodiments, the first cell may be upstream of the second cellin the active manufacturing assembly line.

In some embodiments, at least some of the cell data may correspond to afirst time. At least some of the line production data may correspond toa second time later than the first time. The one or more productionassociations may include a production association between cell datacorresponding to the first time and a production level corresponding tothe second time.

In some embodiments, the processor is configured to: identify theproduction association between the cell data corresponding to the firsttime and the production level corresponding to the second time as one ofthe one or more critical production associations if a difference betweenthe first time and the second time is less than a pre-determined timelimit.

In some embodiments, the at least one input state may include a starvedstate in which the corresponding cell received an undersupply of atleast one input for processing the workpiece.

In some embodiments, the predictive model may include a decision tree.The decision tree may include a plurality of nodes. The processor may beconfigured to generate at least one node corresponding to the one ormore critical production associations.

In some embodiments, each cell may include at least one deviceconfigured to process the workpiece. The processor may be furtherconfigured to receive device data associated with at least one deviceduring the operation of the active manufacturing assembly line. Thedevice data may include, for each device, at least one device state. Theplurality of production associations may include an association betweena device state and the production level of the active manufacturingassembly line.

In accordance with some embodiments, there is provided a method foroptimizing a manufacturing assembly line. The manufacturing assemblyline includes a plurality of cells. Each cell is configured tosuccessively process a workpiece along the manufacturing assembly line.The method involves operating a processor to: receive cell dataassociated with at least one cell of the manufacturing assembly line,the cell data including, for each cell, at least one input state of thatcell and a cell position of that cell within the manufacturing assemblyline; extract feature data from the cell data, the feature dataincluding at least one input state and at least one cell position of atleast one cell; determine a plurality of cell configurations, each cellconfiguration corresponding to one cell and defining a different processfor processing the workpiece by that one cell; determine an efficiencyscore for each cell configuration by applying the extracted feature datato a predictive model generated for predicting a production level of themanufacturing assembly line; determine at least one target cellconfiguration from the plurality of cell configurations based on theefficiency score for each cell configuration; and apply the at least onetarget cell configuration to at least one cell by implementing eachtarget cell configuration to a corresponding cell.

In some embodiments, determining the plurality of cell configurationsmay involve determining a plurality of cell configurations for one cell.Determining the at least one target cell configuration may involvedetermining one target cell configuration for that one cell by selectinga cell configuration with the highest efficiency score from theplurality of cell configurations for that one cell.

In some embodiments, determining the plurality of cell configurationsmay involve determining at least one cell configuration for each cell.Determining the at least one target cell configuration may involvedetermining a target cell configuration for each cell.

In some embodiments, determining the target cell configuration for eachcell may involve: determining a plurality of sets of cellconfigurations, each set of cell configurations including one cellconfiguration for each cell; determining an overall efficiency score foreach set of cell configurations based on the efficiency score of eachcell configuration in that set of cell configurations; and selecting aset of cell configurations with the highest overall efficiency score asthe target cell configuration for each cell.

In some embodiments, determining the efficiency score for each cellconfiguration may involve: determining an input state of at least onecell upstream of a cell corresponding to that cell configuration; anddetermining the efficiency score for that cell configuration based onthe input state.

In some embodiments, determining the efficiency score for each cellconfiguration may involve: determining an input state of at least onecell downstream of a cell corresponding to that cell configuration; anddetermining the efficiency score for that cell configuration based onthe input state.

In some embodiments, determining the efficiency score for each cellconfiguration may involve: determining a production level of themanufacturing assembly line when that cell configuration is applied to acorresponding cell; and determining the efficiency score for that cellconfiguration based on the production level.

In some embodiments, determining the efficiency score for that cellconfiguration may be based on whether the production level meets apredetermined production quota.

In some embodiments, determining the efficiency score for each cellconfiguration may involve: determining a defect level of themanufacturing assembly line when that cell configuration is applied to acorresponding cell; and determining the efficiency score for that cellconfiguration based on the defect level.

In some embodiments, determining the efficiency score for that cellconfiguration may involve: determining at least one of a rework and ascrap cost based on the defect level; and determining the efficiencyscore for that cell configuration based on the at least one of therework and the scrap cost.

In some embodiments, determining the efficiency score for that cellconfiguration may be further based on whether the defect level meets apredetermined production quota.

In some embodiments, each cell may include at least one devicesconfigured to process the workpiece. Each cell configuration may involveat least one device configuration corresponding to a device of acorresponding cell and defining a different process for processing theworkpiece by that device. Applying the at least one target cellconfiguration may involve implementing each corresponding deviceconfiguration at a corresponding device.

In accordance with some embodiments, there is provided a non-transitorycomputer-readable medium including instructions executable on aprocessor for implementing the method.

In accordance with some embodiments, there is provided a system foroptimizing a manufacturing assembly line. The manufacturing assemblyline includes a plurality of cells. Each cell is configured tosuccessively process a workpiece along the manufacturing assembly line.The system includes a processor configured to: receive cell dataassociated with at least one cell of the manufacturing assembly line,the cell data including, for each cell, at least one input state of thatcell and a cell position of that cell within the manufacturing assemblyline; extract feature data from the cell data, the feature dataincluding at least one input state and at least one cell position of atleast one cell; determine a plurality of cell configurations, each cellconfiguration corresponding to one cell and defining a different processfor processing the workpiece by that one cell; determine an efficiencyscore for each cell configuration by applying the extracted feature datato a predictive model generated for predicting a production level of themanufacturing assembly line; determine at least one target cellconfiguration from the plurality of cell configurations based on theefficiency score for each cell configuration; and apply the at least onetarget cell configuration to at least one cell by implementing eachtarget cell configuration to a corresponding cell.

In some embodiments, the processor may be further configured todetermine a plurality of cell configurations for one cell; and determineone target cell configuration for that one cell by selecting a cellconfiguration with the highest efficiency score from the plurality ofcell configurations for that one cell.

In some embodiments, the processor may be further configured todetermine at least one cell configuration for each cell; and determine atarget cell configuration for each cell.

In some embodiments, the processor may be further configured to:determine a plurality of sets of cell configurations, each set of cellconfigurations including one cell configuration for each cell; determinean overall efficiency score for each set of cell configurations based onthe efficiency score of each cell configuration in that set of cellconfigurations; and select a set of cell configurations with the highestoverall efficiency score as the target cell configuration for each cell.

In some embodiments, the processor may be further configured to:determine an input state of at least one cell upstream of a cellcorresponding to that cell configuration; and determine the efficiencyscore for that cell configuration based on the input state.

In some embodiments, the processor may be further configured to:determine an input state of at least one cell downstream of a cellcorresponding to that cell configuration; and determine the efficiencyscore for that cell configuration based on the input state.

In some embodiments, the processor may be further configured to:determine a production level of the manufacturing assembly line whenthat cell configuration is applied to a corresponding cell; anddetermine the efficiency score for that cell configuration based on theproduction level.

In some embodiments, the processor may be configured to determine theefficiency score for that cell configuration based on whether theproduction level meets a predetermined production quota.

In some embodiments, the processor may be configured to: determine adefect level of the manufacturing assembly line when that cellconfiguration is applied to a corresponding cell; and determine theefficiency score for that cell configuration based on the defect level.

In some embodiments, the processor may be further configured to:determine at least one of a rework and a scrap cost based on the defectlevel; and determine the efficiency score for that cell configurationbased on the at least one of the rework and the scrap cost.

In some embodiments, the processor may be further configured todetermine the efficiency score for that cell configuration based onwhether the defect level meets a predetermined production quota.

In some embodiments, each cell may include at least one devicesconfigured to process the workpiece. Each cell configuration may includeat least one device configuration corresponding to a device of acorresponding cell and defining a different process for processing theworkpiece by that device. The processor may be configured to implementeach corresponding device configuration at a corresponding device.

In accordance with some embodiments, there is provided a method forassessing faults in a manufacturing assembly line. The manufacturingassembly line includes a plurality of cells. Each cell is configured tosuccessively process a workpiece along the manufacturing assembly line.The method involves operating a processor to: receive cell dataassociated with at least one cell of the manufacturing assembly line,the cell data including, for each cell, at least one input state of thatcell and a cell position of that cell within the manufacturing assemblyline; extract feature data from the cell data, the feature dataincluding at least one input state and at least one a cell position ofat least one cell; determine a plurality of faults, each faultcorresponding to one cell; determine a priority level for each fault byapplying the extracted feature data to a predictive model; determine atleast one high priority fault from the plurality of faults based on thepriority level for each fault; and generate at least one operator alertbased on the at least one high priority fault.

In some embodiments, determining the plurality of faults may involvedetermining a plurality of faults for one cell. Determining the at leastone high priority fault may involve determining one high priority faultfor that one cell by selecting a fault with the highest priority levelfrom the plurality of faults for that one cell.

In some embodiments, determining the plurality of faults may involvedetermining a plurality of faults for at least two cells, each faultcorresponding to one of the at least two cells. Determining the at leastone high priority fault may involve determining one high priority faultfor one of the at least two cells by selecting a fault with the highestpriority level from the plurality of faults for the at least two cells.

In some embodiments, determining the priority level for each fault mayinvolve: determining an input state of at least one cell upstream of acell corresponding to that fault when that fault occurs at thecorresponding cell; and determining the priority level for that faultbased on the input state.

In some embodiments, determining the priority level for each fault mayinvolve: determining an input state of at least one cell downstream of acell corresponding to that fault when that fault occurs at thecorresponding cell; and determining the priority level for that faultbased on the input state.

In some embodiments, determining the priority level for each fault mayinvolve: determining a production level of the manufacturing assemblyline when that fault occurs at a corresponding cell; and determining thepriority level for that fault based on the production level.

In some embodiments, determining the priority level for that fault maybe based on whether the production level meets a predeterminedproduction quota.

In some embodiments, determining the priority level for each fault mayinvolve: determining a defect level of the manufacturing assembly linewhen that fault occurs at a corresponding cell; and determining thepriority level for that fault based on the defect level.

In some embodiments, determining the priority level for that fault mayinvolve: determining at least one of a rework and a scrap cost based onthe defect level; and determining the priority level for that faultbased on the at least one of the rework and the scrap cost.

In some embodiments, determining the priority level for that fault isfurther based on whether the defect level meets a predeterminedproduction quota.

In some embodiments, each cell may include at least one devicesconfigured to process the workpiece. Each fault may include at least onedevice fault, each device fault corresponding to a device of a cellcorresponding to that fault. The at least one operator alert may begenerated further based on a device fault corresponding to the at leastone high priority fault.

In some embodiments, determining the plurality of faults may involve:determining at least one input state of at least one cell; anddetermining the plurality of faults based on the at least one inputstate.

In accordance with some embodiments, there is provided a non-transitorycomputer-readable medium including instructions executable on aprocessor for implementing the method.

In accordance with some embodiments, there is provided a system forassessing faults in a manufacturing assembly line. The manufacturingassembly line includes a plurality of cells. Each cell is configured tosuccessively process a workpiece along the manufacturing assembly line.The system includes a processor configured to: receive cell dataassociated with at least one cell of the manufacturing assembly line,the cell data including, for each cell, at least one input state of thatcell and a cell position of that cell within the manufacturing assemblyline; extract feature data from the cell data, the feature dataincluding at least one input state and at least one a cell position ofat least one cell; determine a plurality of faults, each faultcorresponding to one cell; determine a priority level for each fault byapplying the extracted feature data to a predictive model; determine atleast one high priority fault from the plurality of faults based on thepriority level for each fault; and generate at least one operator alertbased on the at least one high priority fault.

In some embodiments, the processor may be configured to determine aplurality of faults for one cell; and determine one high priority faultfor that one cell by selecting a fault with the highest priority levelfrom the plurality of faults for that one cell.

In some embodiments, the process may be configured to determine aplurality of faults for at least two cells, each fault corresponding toone of the at least two cells; and determine one high priority fault forone of the at least two cells by selecting a fault with the highestpriority level from the plurality of faults for the at least two cells.

In some embodiments, the processor may be configured to: determine aninput state of at least one cell upstream of a cell corresponding tothat fault when that fault occurs at the corresponding cell; anddetermine the priority level for that fault based on the input state.

In some embodiments, the processor may be configured to: determine aninput state of at least one cell downstream of a cell corresponding tothat fault when that fault occurs at the corresponding cell; anddetermine the priority level for that fault based on the input state.

In some embodiments, the processor may be configured to: determine aproduction level of the manufacturing assembly line when that faultoccurs at a corresponding cell; and determine the priority level forthat fault based on the production level.

In some embodiments, the processor may be configured to determine thepriority level for that fault based on whether the production levelmeets a predetermined production quota.

In some embodiments, the processor may be configured to: determine adefect level of the manufacturing assembly line when that fault occursat a corresponding cell; and determine the priority level for that faultbased on the defect level.

In some embodiments, the processor may be configured to: determine atleast one of a rework and a scrap cost based on the defect level; anddetermine the priority level for that fault based on the at least one ofthe rework and the scrap cost.

In some embodiments, the processor may be further configured todetermine the priority level for that fault based on whether the defectlevel meets a predetermined production quota.

In some embodiments, each cell may include at least one devicesconfigured to process the workpiece. Each fault may include at least onedevice fault, each device fault corresponding to a device of a cellcorresponding to that fault. The processor may be configured to generatethe at least one operator alert based on a device fault corresponding tothe at least one high priority fault.

In some embodiments, the processor may be configured to: determine atleast one input state of at least one cell; and determine the pluralityof faults based on the at least one input state.

BRIEF DESCRIPTION OF THE DRAWINGS

Several embodiments will be described in detail with reference to thedrawings, in which:

FIG. 1 is a block diagram of an example modeling system in communicationwith an example manufacturing assembly line, in accordance with someembodiments; and

FIG. 2A is a block diagram of an example manufacturing assembly line formanufacturing contact lenses;

FIG. 2B is a block diagram of an example manufacturing assembly line formanufacturing automobiles;

FIG. 3 is a flowchart of an example method of operating the modelingsystem of FIG. 1 to train a model, in accordance with some embodiments;

FIG. 4 is an graphical representation of an example model trained by theoperating the modeling system of FIG. 1;

FIG. 5 is a flowchart of another example method of operating themodeling system of FIG. 1 to optimize the configuration of amanufacturing assembly line, in accordance with some embodiments;

FIG. 6 is a flowchart of another example method of operating themodeling system of FIG. 1 to assess faults in a manufacturing assemblyline, in accordance with some embodiments;

FIG. 7 is a graph illustrating an example residual distribution ofassociations for a manufacturing assembly line;

FIG. 8 is a graph illustrating an example frequency distribution ofassociations for a manufacturing assembly line;

FIG. 9 is a graph illustrating an example frequency distribution ofassociations by cell for a manufacturing assembly line;

FIG. 10 is a graph illustrating an example frequency and residualdistribution of associations for a manufacturing assembly line;

FIG. 11 is a graph illustrating an example timespan distribution ofassociations for a manufacturing assembly line;

FIG. 12 is a graph illustrating an example predicted production levelover time of a manufacturing assembly line; and

FIG. 13 is a graph illustrating an example health score distribution bydevice for a manufacturing assembly line.

The drawings, described below, are provided for purposes ofillustration, and not of limitation, of the aspects and features ofvarious examples of embodiments described herein. For simplicity andclarity of illustration, elements shown in the drawings have notnecessarily been drawn to scale. The dimensions of some of the elementsmay be exaggerated relative to other elements for clarity. It will beappreciated that for simplicity and clarity of illustration, whereconsidered appropriate, reference numerals may be repeated among thedrawings to indicate corresponding or analogous elements or steps.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Modern manufacturing assembly lines can involve a large number of highlyconfigurable machines that can produce complex final products. Forexample, manufacturing assembly lines configured to produce complexproducts such as medical devices, electronics, or automobiles, mayinvolve more than hundreds, if not thousands, of processing steps. Toincrease the efficiency of the manufacturing process, manufacturingassembly lines may be organized into a number of subsections orstations, which may be referred to as cells. Each cell can include acollection of one or more related devices for processing a workpiece.

It may be difficult to predict the behavior a manufacturing assemblyline, even if it is organized into one or more cells. Despite asimplified cellular arrangement, a manufacturing assembly line maynevertheless exhibit highly complex, interdependent, and convolutedproperties. Traditionally, understanding the behavior of a manufacturingassembly line typically involves relying on the intuition or “gut-feel”of expert operators who have significant experience dealing with aparticular manufacturing assembly line. However, this may be unreliablewhen changes to the manufacturing assembly line are implemented.Furthermore, no such expert operators may be available when implementinga new manufacturing assembly line.

For example, the production level of a manufacturing assembly line maybe dependent on a large number of factors, including, but not limitedto, the configuration of the cells. Changing the configuration of onecell may affect the efficiency of one or more other cells or even theentire manufacturing assembly line. These interdependencies may make itdifficult to determine an optimal configuration for the manufacturingassembly line. Furthermore, when problems inevitably arise in themanufacturing assembly line, it can be difficult to evaluate whichfaults should be remedied first. For example, it may be unclear whichfaults are critical to the operation of the manufacturing assembly line.

Moreover, it can also be difficult to understand how the quality ofparts produced by the manufacturing assembly line are affected by thesefactors. Complex part routing, incoming components, environment, andother variables may all impact the quality of the production.

The systems and methods described herein can involve modeling thebehavior of a manufacturing assembly line. For example, some embodimentsdescribed herein may involve predicting the production level of amanufacturing assembly line.

The systems and methods described herein may use artificial intelligenceor machine learning methods to train (i.e., generate or build) modelsfor predicting one or more properties of a manufacturing assembly line.For example, some embodiments described herein may involve receivingvarious data associated with a manufacturing assembly line, identifyingdata relevant to the operation of the manufacturing assembly line, andtraining a model using the relevant data. The systems and methodsdescribed herein may generate models that do not rely on explicitinstructions or programming. Instead, the described systems and methodsmay generate models that utilize patterns or inferences determined fromtraining data.

The systems and methods described can also involve using trained modelsfor various purposes. For example, the trained models may be used topredict a production level of a manufacturing assembly line based oncell data associated with cells in that manufacturing assembly line. Insome embodiments, the trained models may be used to optimize theefficiency of a manufacturing assembly line. For example, the trainedmodels may be used to determine the efficiency of various configurationsto identify a target configuration. In some embodiments, the trainedmodels may be used to evaluate faults in the manufacturing assemblyline. For example, the trained models may be used to determine faultsthat should be prioritized to be addressed.

Reference is first made to FIG. 1, which illustrates an example blockdiagram 100 of a modeling system 110 in communication with amanufacturing assembly line 120, an external data storage 108, and acomputing device 104 via a network 102. Although only one manufacturingassembly line 120 and one computing device 104 are shown in FIG. 1, themodeling system 110 can be in communication with a greater number ofmanufacturing assembly lines 120 and/or computing devices 104. Themodeling system 110 can communicate with the manufacturing assemblyline(s) 120 and computing device(s) 104 over a wide geographic area viathe network 102.

The modeling system 110 includes a processor 112, a data storage 114,and a communication component 116. The modeling system 110 can beimplemented with more than one computer server distributed over a widegeographic area and connected via the network 102. The processor 112,the data storage 114 and the communication component 116 may be combinedinto a fewer number of components or may be separated into furthercomponents.

The processor 112 can be implemented with any suitable processor,controller, digital signal processor, graphics processing unit,application specific integrated circuits (ASICs), and/or fieldprogrammable gate arrays (FPGAs) that can provide sufficient processingpower for the configuration, purposes and requirements of the modelingsystem 110. The processor 112 can include more than one processor witheach processor being configured to perform different dedicated tasks.

The communication component 116 can include any interface that enablesthe modeling system 110 to communicate with various devices and othersystems. For example, the communication component 116 can receive celldata generated by the manufacturing assembly line 120 and store the celldata in the data storage 114 or the external data storage 108. Theprocessor 112 can then use the cell data to train a predictive modelaccording to the methods described herein.

The communication component 116 can include at least one of a serialport, a parallel port or a USB port, in some embodiments. Thecommunication component 116 may also include an interface to componentvia one or more of an Internet, Local Area Network (LAN), Ethernet,Firewire, modem, fiber, or digital subscriber line connection. Variouscombinations of these elements may be incorporated within thecommunication component 116. For example, the communication component116 may receive input from various input devices, such as a mouse, akeyboard, a touch screen, a thumbwheel, a track-pad, a track-ball, acard-reader, voice recognition software and the like depending on therequirements and implementation of the modeling system 110.

The data storage 114 can include RAM, ROM, one or more hard drives, oneor more flash drives or some other suitable data storage elements suchas disk drives. The data storage 114 can include one or more databasesfor storing data related to the manufacturing assembly line 120, such ascell data, device data, and line production data. The data storage 114may also store various data related to models trained by the modelingsystem 110.

In some embodiments, the data storage 114 can be used to store anoperating system and programs. For instance, the operating systemprovides various basic operational processes for the processor 112. Theprograms may include various user programs so that a user can interactwith the processor 112 to perform various functions such as, but notlimited to, viewing and/or manipulating the stored data, models, as wellas retrieving and/or transmitting data.

The external data storage 108 can store data similar to that of the datastorage 114. The external data storage 108 can, in some embodiments, beused to store data that is less frequently used and/or older data. Insome embodiments, the external data storage 108 can be a third partydata storage stored with cell, device, or line production data foranalysis by the modeling system 110. The data stored in the externaldata storage 108 can be retrieved by the computing device 104 and/or themodeling system 110 via the network 102.

The computing device 104 can include any device capable of communicatingwith other devices through a network such as the network 102. A networkdevice can couple to the network 102 through a wired or wirelessconnection. The computing device 104 can include a processor and memory,and may be an electronic tablet device, a personal computer,workstation, server, portable computer, mobile device, personal digitalassistant, laptop, smart phone, WAP phone, an interactive television,video display terminals, gaming consoles, and portable electronicdevices or any combination of these.

The network 102 can include any network capable of carrying data,including the Internet, Ethernet, plain old telephone service (POTS)line, public switch telephone network (PSTN), integrated servicesdigital network (ISDN), digital subscriber line (DSL), coaxial cable,fiber optics, satellite, mobile, wireless (e.g. Wi-Fi, WiMAX), SS7signaling network, fixed line, local area network, wide area network,and others, including any combination of these, capable of interfacingwith, and enabling communication between, the modeling system 110, themanufacturing assembly line 120, the external data storage 108, and thecomputing device 104.

The manufacturing assembly line 120 can be any type of assembly line formanufacturing any type of product. For example, the manufacturingassembly line 120 may be configured to manufacture food or beverage,textile, computer or electronic, vehicle, chemical, medical, or otherproducts. The manufacturing assembly line 120 may perform discretemanufacturing processes to produce discrete products or perform processmanufacturing processes to produce bulk or undifferentiated products.The particular arrangement and configuration of the manufacturingassembly line 120 may depend on the type of the product beingmanufactured.

The manufacturing assembly line 120 can include one or more cells 122.Each cell 122 may be a subsection or station of the manufacturingassembly line 120 that is configured to perform a specific processingtask on a workpiece. During operation, a workpiece can travel along themanufacturing assembly line 120 from cell 122 (e.g., Cell 1) to cell 122(e.g., Cell 2). Each cell 122 can progressively process the workpieceuntil a final product is produced.

Each cell 122 can include one or more devices 124 for processing aworkpiece. The processing tasks performed by the devices 124 of aparticular cell 122 can be related to each other. For example, thedevices 124 of a particular cell 122 may perform sub-steps orsub-processes in the overall processing task accomplished by the cell122. The devices 124 for a particular cell 122 can, in some embodiments,be physically located proximate to each other. For example, the devices124 for a particular cell 122 may be housed within a common chassis orshare a common power supply.

The cells 122 can be positioned and connected in various arrangements toform the manufacturing assembly line 120. For example, the cells 122 canbe arranged in a linear fashion, in which the manufacturing assemblyline 120 is formed by a single line of connected cells 122 (e.g., asillustrated in FIGS. 1 and 2A). In some embodiments, the cells 122 canbe arranged in a non-linear manner. For example, the manufacturingassembly line 120 may be formed by two or more parallel lines ofconnected cells 122, which may or may not be connected (e.g., asillustrated in FIG. 2B).

Various types of data can be captured from the manufacturing assemblyline 120 and used by the modeling system 110. For example, dataassociated with cells 122, the devices 124, or the productivity of themanufacturing assembly line 120 may be collected or sampled. Thecaptured data may be stored in the manufacturing assembly line 120, thecomputing device 104, the external data storage 108 and/or the datastorage 114. The stored data may be formatted in various ways, such as,but not limited to, tensors, arrays, and the like. As will be describedin greater detail with reference to FIG. 3-6, the captured data can beretrieved by the modeling system 110 and used to train a model or usedwith a trained model to make predictions regarding the manufacturingassembly line 120.

The data captured from the manufacturing assembly line 120 can includedata associated with one or more cells 122, which may be referred hereinas cell data. The cell data may include data related to the position ofone or more cells 122 within the manufacturing assembly line 120. Thecell data may define the position of one or more cells 122 relative toone or more other cells 122. For example, a cell position may specifythat a particular cell 122 is upstream or downstream of one or moreother cells 122 (e.g., ahead or behind in the manufacturing assemblyline 120). In some embodiments, the cell data may define one or morecell positions absolutely within the manufacturing assembly line 120.For example, in a linear arrangement, each cell may be assigned anumerical cell position (e.g., 1 for the first cell, 2 for the secondcell, etc.).

The cell data may also include data related to the state of one or morecells 122 during the operation of the manufacturing assembly line 120.The state of a particular cell 122 can specify the status or conditionof that cell 122. For example, a cell state may indicate that aparticular cell 122 is running normally, running but experiencing afault, not running all together, etc. A cell state may also identify theinput state of a particular cell 122. For example, the input state mayspecify that a particular cell 122 is starved or underfed (e.g., whenthat cell 122 has not received one or more inputs required for that cell122 to process the workspace) or overfed (e.g., where that cell 122 hasreceived an oversupply of one or more inputs).

The cell data may also include data related to the cell configurationfor one or more cells 122. A cell configuration may define variousparameters or settings that define the processing task performed by thatcell 122 on a workpiece. For example, the cell configuration may definethe type of processing, the length of the processing, etc.

The data captured from the manufacturing assembly line 120 can alsoinclude data associated with one or more devices 124 of the cells 122,which may be referred herein as device data. Similar to the cell data,the device data can include data related to the state of one or moredevices 124 during the operation of the manufacturing assembly line 120,such as, whether a particular device is running normally, experiencing afault, not running, starved, overfed, etc. However, in contrast with thecell data, the device data may include data that has a higher degree ofgranularity. For example, the device data may include data related tothe speed, cycle time, temperature, humidity, etc. of one or moredevices 124. The device data may also include data related to the deviceconfiguration for one or more devices 124, defining various parametersof settings for the processing completed by those devices 124.

The data captured from the manufacturing assembly line 120 can alsoinclude data associated with the productivity of the manufacturingassembly line 120 during operation, which may be referred herein as lineproduction data. The line production data may include data regarding theoverall production level of the manufacturing assembly line 120. Forexample, the line production data can define the number of finishedproducts produced by the manufacturing assembly line 120 in a givenperiod of time. The line production data may also include dataindicating the production level of a particular portion of themanufacturing assembly line 120. For example, the line production datacan define the number of processed workpieces produced by one or morecells 122 or one or more devices 124 in a given period of time. The lineproduction data may also include data related to the defect level orquality level of the manufacturing assembly line 120 (or a portionthereof). For example, the line production data may define the number ofdefective products produced by the line, a particular cell 122, or aparticular device 124, in a given period of time.

The captured data may include datasets that are collected over a periodof time. For example, the data may include timestamps corresponding towhen a particular data point was sampled or measured. For example, thedata may include cell data that represents the state of one or morecells 122 at various points in time. Similarly, the data may alsoinclude line production data that represents the production level of themanufacturing assembly line 120 (or cell 122 or device 124) at varioustime points. Likewise, the data may also include device data thatrepresents the state of one or more devices 124 at different times. Thedata may include different types of data that have the same timestamp(e.g., correspond to the same, or approximately the same, time. Forexample, the data may include a cell state corresponding to a particulartime, and a production level corresponding to the same time.

In some embodiments, the captured data may data that is received fromone or more external sources (i.e., external from the manufacturingassembly line 120). For example, the data may include subcomponentassembly information, environmental factors, supplier information,material composition data, facility information, ERP and transactioninformation, shipping data, component packaging data, and the like.

In various embodiments, the captured data may include buffering time,buffering levels, warehousing locations and times, manufacturing tool ordevice information (e.g., machining toolsets, mold toolsets, or anytools or devices used to manufacture or assemble the product), qualitydata (e.g., vision images, part videos, measurements, part dimensions,part tolerances, part performance characteristics, etc.), and the like.

The data captured from the manufacturing assembly line 120 may begenerated by the manufacturing assembly line 120 itself (and/or by thecells 122 and/or the devices 124). For example, the manufacturingassembly line 120 may include various sensors (not shown) configured tomeasure various properties of the manufacturing assembly line 120. Insome embodiments, the data may be generated by an external device, suchas the computing device 104, configured to monitor the manufacturingassembly line 120. In some cases, a human operator may inspect one ormore portions of the manufacturing assembly line 120, and electronicallyinput the data into the computer device 104, the manufacturing assemblyline 120, or the modeling system 110.

Different types of data associated with the manufacturing assembly line120 may be captured at different sampling rates. For example, dataassociated with the cells 122 may be captured every 200 milliseconds,whereas data associated with the devices may be captured every 50milliseconds. The data may be sampled in a manner such that differenttypes of data can be temporally aligned. For example, some types of datamay be sampled at a relatively higher rate than other types of data sothat at least some of the oversampled data will correspond to the same(or approximately the same) time as data captured at a lower samplingrate. Oversampling data to temporally align the data may provide moreaccurate data points than merely interpolating data sampled at lowersampling rates or other similar methods.

Two examples of a manufacturing assembly line 120 will be now bedescribed with reference to FIGS. 2A and 2B.

FIG. 2A shows a block diagram of an example manufacturing assembly line120A for manufacturing contact lens. The manufacturing assembly line120A can include a number of cells 122A, such as, but not limited to, amold forming cell 202, a lens forming cell 204, a lens retrieval cell206, and a packaging cell 208. Each of the cells 122A can include one ormore devices, which are omitted from FIG. 2A for ease of illustrationand exposition.

The mold forming cell 202 can produce molds for shaping the contactlenses. The mold forming cell 202 can receive mold materials and createthe molds from the mold materials. The mold forming cell 202 can alsoreceive used molds from the lens retrieval cell 206. The mold formingcell 202 can include various devices for melting the mold materials,filling the molds, processing the molds, inspecting the formed moldsetc. The mold production cell 202 can send the finished molds to thelens forming cell 204.

The lens forming cell 204 can receive forming material, as well as themolds from the mold forming cell 202, and produce lenses from theforming material using the molds. The lens forming cell 204 can includevarious devices for dispensing the forming material, aligning the molds,closing the molds, curing the forming material, etc. The lens formingcell 204 can send the finished lenses (attached to the molds) to thelens retrieval cell 206.

The lens retrieval cell 206 can receive the lenses and molds from thelens forming cell 204 and prepare the lens for packaging. The lensretrieval cell 206 can include various devices for mold processing,removing the lenses, hydrating the lenses, inspecting the lenses, etc.The lens retrieval cell 206 can send the lenses to the packaging cell208 and send the molds to the mold forming cell 202.

The packaging cell 208 can receive packaging materials, as well as thelenses from the lens retrieval cell 206, and package the lenses usingthe packaging materials. The packaging cell 208 can include variousdevices for packaging the lenses, filing the packaging with saline,sealing the packaging, labeling the packaging, sterilizing thepackaging, inspecting the packaging, etc. The packaging cell 208 canoutput a final packaged contact lens product from the manufacturingassembly line 120A.

FIG. 2B shows a block diagram of an example manufacturing assembly line120B for manufacturing automobiles. The manufacturing assembly line 120Bcan include a number of cells 122A, such as, but not limited to, a bodyassembly cell 252, a painting cell 254, an interior assembly cell 256, achassis assembly cell 258, a mating cell 260, a final assembly cell 262,and a rework cell 264. Each of the cells 122B can include one or moredevices, which are omitted from FIG. 2B for ease of illustration andexposition.

The body assembly cell 252 can create the body, or exterior shell of anautomobile. The body assembly cell 252 receives parts for the body, suchas panels and braces, and assembles the parts into a body. The bodyassembly cell 252 can include various devices for positioning parts,welding parts, inspecting the body, etc. The body assembly cell 252 cansend the assembled body to the painting cell 254.

The painting cell 254 can receive paint, as well as the body from thebody assembly cell 252, and apply the paint to the body. The paintingcell 254 can include various devices for cleaning the body, priming thebody, dispensing paint onto the body, curing the paint, inspecting thepainted body, etc. The painting cell can send the painted body to theinterior assembly cell 256.

The interior assembly cell 256 can build the interior of the vehicle,such as the dashboard, seats, etc. The interior assembly cell 256 canreceive parts for the interior, as well as the painted body from thepainting cell 254, and assemble the interior. The interior assembly cell256 can include various devices for positioning parts, assembling theparts, inspecting the assembled parts, etc. The interior assembly cell256 can send the assembled body to the mating cell 260.

The chassis assembly cell 258 can build the chassis, or structural frameof the automobile, and the powertrain. The chassis assembly cell 258 canreceive parts, such as beams and rails and assemble the parts into thechassis. The chassis assembly cell 258 can also receive parts, such asthe engine or transmission, and assemble the parts into the power train.The chassis assembly cell 258 can include various devices forpositioning parts, welding parts, inspecting the chassis, etc. Thechassis assembly cell 258 can send the assembled chassis to the matingcell 260. The chassis assembly cell 258 may operate in parallel to thebody assemble cell 252, the painting cell 254, and the interior assemblycell 256.

The mating cell 260 can receive the chassis from the chassis assemblycell 258, and the body from the interior assembly cell 256 and fix thetwo assemblies together. The mating cell 260 can include various devicesfor positioning the body/chassis, welding the body/chassis, inspectingthe final assembly, etc. The mating cell 260 can then send the assemblyto the final assembly cell 262.

The final assembly cell 262 can add various parts to the receivedassembly to finalize the automobile, such as batteries, tires, etc. Thefinal assembly cell receives the final parts, as well as the body andchassis assembly from the mating cell 260, and installs the final partsinto the assembly. The final assembly cell 262 can include variousdevices for installing the final parts, inspecting the automobile, etc.The final assembly cell 262 can output the finished automobile from themanufacturing assembly line 120B.

Optionally, the manufacturing assembly line 120B may include a reworkcell 264 that can receive an unfinished assembly (i.e., a workpiece)from any other cell 122B in the manufacturing assembly line 120B. Therework cell 264 can include various devices for fixing any defects inthe processing performed by any other cell 122B. In some embodiments,the manufacturing assembly line 120B may send defective workpieces backto the cell responsible for the defect to be reworked.

As shown in FIGS. 2A and 2B, different manufacturing assembly lines120A, 120B can have different arrangements and configurations, dependingon the type of product produced. For example, contact lenses may requirerelatively low cost and simple processing to manufacture. Accordingly, alinear arrangement may be suitable. In contrast, an automobile mayrequire relatively higher cost and more complex processing. As a result,an automobile may require non-linear arrangement, for example, forreworking defective workpieces.

The example manufacturing assembly lines 120A and 120B will now be usedto illustrate various examples of the operation of the modeling system110.

Reference is now made to FIG. 3, which shows a flowchart of an examplemethod 300 of operating the modeling system 110. The method 300 can beimplemented by the modeling system 110 to train a predictive model topredict one or more properties of a manufacturing assembly line 120.

At 302, the processor 112 can receive data associated with amanufacturing assembly line 120. The processor 112 can receive the datafrom various sources and in various formats. For example, the processor112 may receive the data from the data storage 114, the external datastorage 108, the computing device 104, and/or the manufacturing assemblyline 120. The processor 112 can receive various data associated with thecells 122, the devices 124, and/or the productivity of the manufacturingassembly line 120 (e.g., the productivity level or product qualitylevel).

For example, continuing with the example of the manufacturing assemblyline 120A, the processor 112 may receive cell data that includes one ormore input states of each cell 122A (e.g., the mold forming cell 202,the lens forming cell 204, the lens retrieval cell 206, and thepackaging cell 208) and the positions of the cells 122A within themanufacturing assembly line 120A. The processor 112 may also receiveproduction data that includes one or more production levels of themanufacturing assembly line 120A. The processor 112 may also receiveproduction data that includes the product quality level of the partsbeing produced by the manufacturing assembly line 120A.

The data received by the processor 112 may include subsets of data thatcorrespond to each other. Some of the received data may be associatedwith the same (or approximately the same) point in time. For example,for the manufacturing assembly line 120A, the received data may includethe input state of one or more cells 122 at a particular time and aproduction level at the same time.

At 304, the processor 112 can determine one or more associations betweenthe received data. The one or more associations may define potential orsuspected relationships, connections, or correspondences betweenportions of the data. The associations may define possible correlationsor causal relationships between portions of the cell data, the devicedata, and/or the line production data. For example, the associations mayinclude but are not limited to, associations between cell states andproduction levels, associations between cell positions and productionlevels, associations between device states and production levels,associations between cell states and other cell states, associationsbetween device states and cells states, etc.

For example, continuing with the example of the manufacturing assemblyline 120A, the associations may include one or more productionassociations between data associated with one or more cells 122A and aproduction level of the manufacturing assembly line 120A or a productquality level of the manufacturing assembly line 120A.

The associations may include associations between data that correspondto the same (or approximately the same) time. For example, theassociations may include an association between cell data and aproduction level sampled at the same (or approximately the same) time.The associations may also include associations between data thatcorrespond to different times. For example, the associations may includean association between cell data corresponding to a first time and aproduction level corresponding to a second time later than the firsttime.

The associations may also include multiple associations corresponding tothe same data. For example, the associations may include an associationbetween a first cell state and a particular production level, and anassociation between a second cell state and the same production level.In another example, the associations may include an association betweena particular cell state and a first production level and the same cellstate and a second production level.

At 306, the processor 112 can determine one or more criticalassociations to the operation of the manufacturing assembly line 120. Itshould be appreciated that not all of the identified associationsidentified at 304 may define relationships relevant to the overallproduction level of the manufacturing assembly line 120. The processor112 can evaluate each of the associations to identify the criticalassociations. The processor 112 can evaluate the one or moreassociations in various ways.

For example, continuing with the example of the manufacturing assemblyline 120A, the input state of some of the cells 122A may be morerelevant to the production level of the manufacturing assembly line120A. For instance, input state of the mold forming cell 202 may have alarger impact on production than the packaging cell 208, because a delayin the mold forming cell 202 may delay one or more downstream cells. Theprocessor 112 may therefore identify an association between an inputstate of the mold forming cell 202 and a production level of themanufacturing assembly line 120A as a critical association.

In some embodiments, the processor 112 may identify one or morestatistically significant associations as the one or more criticalassociations. The processor 112 may perform various types of statisticaltesting to determine the statistical significance of the associations.For example, the processor 112 may determine the probability value ofeach potential association being not actually associated. The processor112 can then select the associations having a probability value that isless than or equal to a predetermined significance level as the criticalassociations. Alternatively, the processor 112 may determine thecritical associations based on a residual (e.g., difference) between theprobability value and the significance level. For example, an exampledistribution of residuals is shown in FIG. 7 in plot 700. The processor112 can select the associations having a residual below a predeterminedlevel as the critical associations.

In some embodiments, the processor 112 may determine the one or morecritical associations based on the frequency of the associations. Forexample, continuing with the example of the manufacturing assembly line120A, each association may correspond to one or more cells 122A. Theprocessor 112 may determine the cells corresponding to the greatestnumber of associations and select associations corresponding to thosecells as the critical associations. For example, an example distributionof association frequency is shown in FIG. 8 in plot 800. An exampledistribution of the cells associated with the highest frequency ofassociations (e.g., corresponding to the one or more criticalassociations) is shown in FIG. 9 in plot 900.

In some embodiments, the processor 112 may determine the one or morecritical associations based on both the residuals and the frequency ofthe associations. For example, an example distribution of residuals andfrequency for a set of associations is shown in FIG. 10 in plot 1000.The processor 112 can select the associations having a residual andfrequency above a predetermined level as the critical associations.

In some embodiments, the processor 112 may identify the one or morecritical associations based on the timestamps of the data correspondingto the associations. For example, for an association between a cellstate corresponding to a first time and a production level correspondingto a second time, the processor 112 may determine the difference in time(if any) between the first and second times. The processor 112 maydetermine that the association is critical association based on whetherthe time difference exceeds a pre-determined time period. An exampledistribution of associations based on timespan is shown in FIG. 11 asplot 1100. As shown in plot 1100, the distribution of associationsgreatly decreases at approximately 2 hours.

At 308, the processor 112 can retrieve the data associated with thecritical associations. The processor 112 may not retrieve all of thedata received by the processor 112 at 302, depending on the criticalassociations. For example, continuing with the example of themanufacturing assembly line 120A, the processor 112 may retrieve dataassociated with the mold forming cell 202, but not data associated withthe packaging cell 208, in accordance with the critical associations.The processor 112 may retrieve the relevant data from the data storage114, the external data storage 108, the computing device 104, and/or themanufacturing assembly line 120.

At 310, the processor 112 can train a model with the retrieved data topredict one or more properties of a manufacturing assembly line 120.Training the model can involve the processor 112 generating or buildinga predictive model based on the retrieved data. It should be appreciatedthat various types of training may be performed by the processor 112.For example, the processor 112 may employ supervised learning,unsupervised learning, semi-supervised learning, reinforcement learning,and/or other training techniques. The training can generate models basedon patterns and inferences detected from the training data, instead ofexplicitly programing or instructing the model to make specificpredictions. The processor 112 may train various types of models, suchas, but not limited to, neural networks, Bayesian networks, decisiontrees, support vector machines, regression analyses, genetic algorithms,random forest, LK-means, deep learning, anomaly detection and root causeanalysis. In various embodiments, regression models may be preferred. Invarious embodiments, the trained model may be updated using variousfeatures or tensors. That is, as new data points (e.g., tensors) becomeavailable to the system, the trained models can be updated using the newdata.

The trained model can then be used to predict one or more properties ofa manufacturing assembly line 120. The inputs and outputs of the trainedmodel can depend on the data used to train the model. For example,continuing with the example of the manufacturing assembly line 120A, themodel may be trained to predict the production level of themanufacturing assembly line 120A based on the input states and positionsof the cells 122A, using training data from the manufacturing assemblyline 120A, which can include cell input states, cell positions, andassociated production levels. As another example, the model may be usedto predict the quality level of the parts produced by the manufacturingassembly line 120A based on the input states and positions of the cells122A.

Referring now to FIG. 4, there is shown an example predictive model 400.The predictive model 400 illustrated in FIG. 4 is structured as adecision tree model generated by the modeling system 110 in accordancewith the method 300. It will be understood that other structures for thepredictive model 400 can be applied.

The decision tree model 400 can include a number of nodes 410 and anumber of branches 412 connected to the nodes 410. Some of the nodes 410can represent a test for a particular property of a manufacturingassembly line 120. For example, continuing with the example of themanufacturing assembly line 120A, a node 410 may represent adetermination of an input state of the mold forming cell 202. Thebranches 412 connected to that node 410 can represent an outcomefollowing node 410. For example, the branches can represent variousstates, such as, but not limited to, “starved”, “stuffed”, and “normal”.Some of the nodes 410 can represent a prediction or a decision. Forexample, a node may represent various production levels, such as “100%production level”, “75% production level”, etc. The nodes 410 cancorrespond to the data used to train the model 400. For example, some ofthe nodes 410 may correspond to the one or more critical associations.

The modeling system 110 can then apply the predictive model 400 to othermanufacturing assembly lines 120 to predict one or more properties ofthe manufacturing assembly line.

Referring now to FIG. 12, an example output of a trained model is shownas plot 900. More specifically, plot 900 shows a predicted productionlevel of a manufacturing assembly line 120 over time. As shown in FIG.9, the accuracy of the predictions generated by the predictive model mayvary over time. For example, the accuracy of the predicted productionlevel may decrease over time, as the time span of the predictionincreases.

It should be appreciated that the modeling system 110 may producevarious types of models. For example, the modeling system 110 may traina model to predict the input states of one or more cells 122 based onthe input states of one or more other cells 122. In some embodiments,the modeling system 110 may train a model to predict the input states ofone or more cells 122 based on the device states of one or more devices124. In another example embodiment, the modeling system 110 may train amodel to predict the productivity level of a manufacturing assembly line120 based on the configuration of one or more cells 122 or one or moredevices 124. In another example embodiment, the modeling system 110 maytrain a model to predict the defect level of a manufacturing assemblyline 120 based on the configuration of one or more cells 122 or one ormore devices 124. Accordingly, the modeling system 110 may train variousmodels capable of predicting various properties of a manufacturingassembly line 120, based on various other properties.

In some embodiments, a model may be trained using data associated with afirst manufacturing assembly line 120 to predict properties of a second,different manufacturing assembly line 120. The manufacturing assemblyline 120 which is used to collect training data may be referred hereinas an active manufacturing assembly line or a training manufacturingassembly line. For example, continuing with the examples of themanufacturing assembly lines 120A and 120B, a model may be trained usingtraining data collected from the manufacturing assembly line 120A. Thepredictive model generated from the training data collected from themanufacturing assembly line 120A may then be used to predict thebehavior of the manufacturing assembly line 120B. Although the physicalprocessing performed by two different manufacturing assembly lines maybe different (e.g., assembling an automobile, vs synthesizing a contactlens), at least some behavior of the cells 122 in the manufacturingassembly line 120A, 120B may be similar and may therefore be predictedusing the predictive model generated from the training data collectedfrom the manufacturing assembly line 120A.

The trained models can be used by the modeling system 110 in variousways, depending on the type of model. For example, a model trained topredict production levels may be used to identify bottlenecks or otherinefficiencies in a manufacturing assembly line 120. Various types ofroot cause analysis or other types of troubleshooting may be used toidentify and correct the inefficiencies in the manufacturing assemblyline 120 identified by the model. In another example, a model trained topredict production levels may be used to identify underperformingsections of a manufacturing assembly line 120 and that may requiremaintenance. Various types of predictive maintenance can be performed onthese sections to maximize production. As another example, a modeltrained to predict product quality levels may be used to identifyproblematic portions of the manufacturing assembly line causing defectsin the produced parts.

Two other example methods of using the predictive models will now bedescribed with reference to FIGS. 5 and 6.

FIG. 5 shows a flowchart of another example method 500 of operating themodeling system 110. The method 500 can be implemented by the modelingsystem 110 to use a predictive model to optimize the configuration of amanufacturing assembly line 120.

At 502, the processor 112 can receive data associated with amanufacturing assembly line 120. The processor 112 can receive variousdata associated with the cells 122, the devices 124, and/or theproductivity of the manufacturing assembly line 120. For example, in theexample of the manufacturing assembly line 120A, the processor 112 mayreceive cell data that includes one or more input states of each cell122A and the positions of the cells 122A within the manufacturingassembly line 120A.

At 504, the processor 112 can extract feature data from the receiveddata. The data extracted by the processor 112 may depend on the type ofpredictive model used. The feature data extracted by the processor 112may correspond to the data used to train the model. For example, if themodel was trained using cell input states and cell positions, theprocessor 112 may extract input states and cell positions as featuredata.

At 506, the processor 112 can determine a number of cell configurations.A cell configuration can include various parameters or settings for acell that modify the processing of a workpiece by that cell. Forexample, continuing with the example of the manufacturing assembly line120A, a cell configuration for the mold forming cell 202 may modify thetemperature for curing the molds. As another example, a cellconfiguration for the lens forming cell 204 may modify the amount offorming material dispensed for each lens.

A cell configuration may also include one or more device configurationsfor the devices of the respective cell. A device configuration caninclude various parameters or settings for a device that modify theprocessing of a workpiece by that device. For example, continuing withthe example of the manufacturing assembly line 120A, a cellconfiguration for the lens forming cell 204 may include deviceconfigurations for a dispensing device or a mold positioning device ofthe lens forming cell 204.

The processor 112 can determine the cell configurations in various ways.In some embodiments, the processor may receive cell configurations whichthe user wishes to evaluate. For example, a user may electronicallyinput the cell configurations into the computing device 104, themanufacturing assembly line 120, and/or the modeling system 110.

The cell configurations determined by the processor 112 may all be forthe same cell. In some embodiments, the cell configurations determinedby the processor 112 may be for a number of different cells.

At 508, the processor 112 can determine efficiency scores for the cellconfigurations. The processor 112 can determine the efficiency score ofa single cell configuration, or the efficiency score of a set of cellconfigurations. The efficiency score for a single cell configuration ora set of cell configurations can be determined based on one or morepredicted properties of the manufacturing assembly line 120 when thatcell configuration or set of cell configurations is implemented. Theprocessor 112 may use the extracted feature data and a predictive modelto predict one or more properties.

For example, the processor 112 may use a predictive model to predict aproduction level of the manufacturing assembly line 120 when one or morecell configurations are implemented. Continuing with the example of themanufacturing assembly line 120A illustrated in FIG. 2A, the predictivemodel may be used to determine the effect of one or more cellconfigurations that increase the curing time used by the mold formingcell 202. For example, increasing the curing time may decrease the rateof mold production, which may decrease the production level of themanufacturing assembly line 120A. The processor 112 may assign arelatively low efficiency score to these configurations due to thedecrease in production level.

In some embodiments, the processor 112 may use a model to predict adefect level of the manufacturing assembly line 120 when the one or morecell configurations are implemented. Continuing with the example of themanufacturing assembly line 120A, one or more cell configurations thatincrease the curing time may also decrease the number of defective moldsproduced that may cause defects in forming the contact lenses,decreasing the overall defect level of the manufacturing assembly line120A. The processor 112 may assign a relatively high efficiency score tothese configurations due to the decrease in defect rate.

The processor 112 may also use the predicted properties to determinewhether the manufacturing assembly line 120 will meet a production quotaand determine the efficiency score based on whether the production quotawill be met. For example, the processor 112 may determine a productionlevel or defect level of the manufacturing assembly line and determinebased on the production level or defect level whether the productionquota will be met. If the predicted production of the manufacturingassembly line 120 does not meet the production quota, the processor 112may assign a relatively low efficiency score to these configurations.

In some embodiments, the processor 112 may also use the predictedproperties to determine a cost for reworking or scrapping the defectiveproducts and determine the efficiency score based on the cost. Forexample, the processor 112 may determine the number of defectiveproducts produced based on a predicted defect rate and determine thecost for reworking or scrapping the defective products. If the rework orscrap cost is relatively high, the processor 112 may assign a relativelylow efficiency score to these configurations.

In some embodiments, the efficiency score for a cell configuration orset of cell configurations can be determined based on the predictedinput state of another cell 122 when the cell configuration or set ofcell configurations is applied. Continuing with the example of themanufacturing assembly line 120A, one or more cell configurations forthe lens forming cell 204 may increasing the lens curing time, which mayaffect the input state of upstream or downstream cells 122A. Forexample, this may cause the lens retrieval cell 206 or the mold formingcell to become starved. The processor 112 can assign a low efficiencyscore to these configurations if they negatively affect the input statesof other cells 122.

At 510, the processor 112 can determine at least one target cellconfiguration. The target cell configuration can be determined based onthe efficiency scores of the cell configurations.

In some embodiments, the processor 112 can determine a single targetcell configuration by identifying a cell configuration associated withthe highest efficiency score. For example, continuing with the exampleof the manufacturing assembly line 120A, the processor 112 can evaluatea number of different configurations for the mold forming cell 202. Eachconfiguration may correspond to a different mold curing time or adifferent curing temperature, etc. The processor 112 can select theconfiguration of the mold forming cell 202 with the highest efficiencyscore as the target cell configuration.

In some embodiments, the processor 112 may determine a set of targetcell configurations by identifying a set of cell configurations with thehighest efficiency score. For example, continuing with the example ofthe manufacturing assembly line 120A, each set of configurations caninclude one configuration for each cell 122A. For instance, each set ofcell configuration may define a curing time for the mold forming cell202, a forming material quantity for the lens forming cell 204, ahydration amount for the lens retrieval cell 206, a sanitation time forthe packaging cell 208, etc. The processor 112 can select the set ofconfigurations with the highest efficiency score as the set of targetconfigurations.

The one or more target cell configurations (or target deviceconfigurations) can then be implemented at the corresponding cells (ordevices). By selecting the target configurations based on efficiencyscores determined using a trained model, the modeling system 110 candetermine configurations that may improve the efficiency of themanufacturing assembly line 120.

Reference is now made to FIG. 6, which shows a flowchart of anotherexample method 600 of operating the modeling system 110. The method 600can be implemented by the modeling system 110 to use a predictive modelto evaluate and prioritize faults at a manufacturing assembly line 120.

At 602, the processor 112 can receive data associated with amanufacturing assembly line 120. The processor 112 can receive variousdata associated with the cells 122, the devices 124, and/or theproductivity of the manufacturing assembly line 120. For example,continuing with the example of the manufacturing assembly line 120A, theprocessor 112 may receive cell data that includes one or more inputstates of each cell 122A and the positions of the cells 122A within themanufacturing assembly line 120A.

At 604, the processor 112 can extract feature data from the receiveddata. The data extracted by the processor 112 may depend on the type ofpredictive model used. The feature data extracted by the processor 112may correspond to the data used to train the model. For example, if themodel was trained using cell input states and cell positions, theprocessor 112 may extract input states and cell positions as featuredata.

At 606, the processor 112 can determine a number of faults. Each faultcan correspond to a cell 122 in a manufacturing assembly line 120. Afault may indicate the occurrence of one or more errors, defects, orother failures at the corresponding cell 122. For example, continuingwith the example of the manufacturing assembly line 120A, a fault forthe mold forming cell 202 may involve a failure in melting or shapingthe molding materials. The faults may also correspond to a device forthat cell.

The processor 112 may determine the faults based on the input states ofthe cells. For example, continuing with the example of the manufacturingassembly line 120A, the processor 112 may identify a fault with the moldforming cell 202 when it has an input state of starved. In someembodiments, the processor 112 may determine the faults based on theproduction level or fault level of the cells 122. For example,continuing with the example of the manufacturing assembly line 120A, theprocessor 112 may determine that a particular cell 122 is faulted whenthe production level for that cell 122 is below a predetermined level orwhen the defect level for that cell 122 is above a predetermined level.

The processor 112 may determine the faults based on the received data.In some embodiments, the processor 112 may determine the faults based onpredictions made by a predictive model. For example, the input states,production levels, and/or defect levels may be determined by theprocessor 112 from the received data or from predictions generated froma trained model using the received data.

The faults determined by the processor 112 may all be for the same cell.In some embodiments, the faults determined by the processor 112 may befor a number of different cells.

At 608, the processor 112 can determine a priority level for the faults.The processor 112 can determine the priority level of a single fault, orthe priority level of a set faults. The priority level for a singlefault or a set of faults can be determined based on one or morepredicted properties of the manufacturing assembly line 120 when thatfault or set of faults is occurring. The processor 112 may use theextracted feature data and a predictive model to predict one or moreproperties.

For example, the processor 112 may use a predictive model to predict aproduction level of the manufacturing assembly line 120 when one or morefaults are occurring. Continuing with the example of the manufacturingassembly line 120A, a predictive model may be used to determine theeffect of a malfunctioning heating device of the mold forming cell 202on the manufacturing assembly line 120A. For example, erroneouslydecreasing the curing temperature may decrease the rate of moldproduction, which may decrease the production level of the manufacturingassembly line 120A. The processor 112 may assign a relatively highpriority score to these faults due to the decrease in production level.

In some embodiments, the processor 112 may use the predictive model topredict a defect level of the manufacturing assembly line 120 when theone or more faults are occurring. In the example of the manufacturingassembly line 120A, one or more faults that decrease curing temperaturemay also increase the number of defective molds produced that may causedefects in forming the contact lenses, increasing the defect level ofthe manufacturing assembly line 120A. The processor 112 may assign arelatively high priority score to these faults due to the increase indefect rate.

The processor 112 may also use the predicted properties to determinewhether the manufacturing assembly line 120 will meet a production quotaand determine the priority level based on whether the production quotawill be met. For example, the processor 112 may determine a productionlevel or defect level of the manufacturing assembly line and determinebased on the production level or defect level whether the productionquota will be met. If the predicted production of the manufacturingassembly line 120 does not meet the production quota, the processor 112may assign a relatively high priority level to these faults.

In some embodiments, the processor 112 may also use the predictedproperties to determine a cost for reworking or scrapping the defectiveproducts and determine the priority level based on the cost. Forexample, the processor 112 may determine the number of defectiveproducts produced based on a predicted defect rate and determine thecost for reworking or scrapping the defective products. If the rework orscrap cost is relatively high, the processor 112 may assign a relativelyhigh priority level to these faults.

In some embodiments, the priority level for a fault or set of faults canbe determined based on the predicted input state of another cell whenthe fault or set faults is occurring. In the example of themanufacturing assembly line 120A, one or more faults for the lensforming cell 204 may increase the lens curing time, which may affect theinput state of upstream or downstream cells. For example, this may causethe lens retrieval cell 206 or the mold forming cell to become starved.The processor 112 can assign a high priority level if these faultsnegatively affect the input states of other cells 122.

At 610, the processor 112 can determine at least one high priorityfault. The high priority faults can be determined based on the prioritylevel of the faults.

In some embodiments, the processor 112 can determine a single highpriority fault by identifying a fault associated with the highestpriority. For example, with respect to the example manufacturingassembly line 120A, the processor 112 can evaluate a number of differentfaults for the mold forming cell 202. The faults may correspond todifferent errors in the same device, or different errors across multipledevices for that cell. The processor 112 can select the fault of themold forming cell 202 with the highest priority as the high priority.

In some embodiments, the processor 112 may determine a set of highpriority faults by identifying a set of faults with the highestpriority. In the example manufacturing assembly line 120A, each set offaults can include one fault for each cell 122A. For instance, each setof faults may define a curing error for the mold forming cell 202, adispensing fault for the lens forming cell 204, a hydration failure forthe lens retrieval cell 206, a sanitation inconsistency for thepackaging cell 208, etc. The processor 112 can identify the set offaults with the highest priority score as the set of high priorityfaults.

The identified one or more high priority faults can then be used togenerate one or more operator alerts. The operator alerts can beelectronic messages that are sent to various users in a manufacturingassembly line 120. For example, the operator alerts may be sent to anddisplayed on the computing device 104 or the manufacturing assembly line120. The operator alerts can instruct an operator to remedy theidentified high priority faults. By identifying high priority faultsusing the predictive model, the modeling system 110 can evaluate faultswith regard to relative priority and remedy the high priority faults.

It should be appreciated that the high priority faults may not benecessarily correspond to the cells 122 with the greatest degree ofdisrepair or lowest performance. For example, the high priority faultsmay be faults that have a relatively large effect on the productivity ofthe manufacturing assembly line 120. For instance, referring to FIG. 13,there is shown a plot 1300 illustrating a distribution of heath scoresfor various devices 124. A health score may correspond to theperformance of a particular device 124. Devices associated by highpriority faults are generally indicated in FIG. 13 as 1302. As shown inFIG. 13, the devices 122 corresponding to the lowest health scores maynot necessarily correspond to the high priority faults. This may be thecase, for example, when some devices with a low health score do not havea large impact on the overall productivity of the manufacturing assemblyline 120.

In various embodiments, predictions generated by the predictive modelsdescribed herein may be accompanied with a causal analysis to determinea causal relationship and provide insights to the predictions, providinga means to improve production of the manufacturing assembly line orbehavior of the equipment of the manufacturing assembly line. In variousembodiments, the predictive models described herein may predict when astop event or production event transitions from a minor or insignificantimpact to the process to a significant impact to the process or behaviorof the equipment. In a production environment, there may be manyinterruptions and, if resolved in a timely fashion, these interruptionsmay have a minor or no impact on production. The predictive modelsdescribed herein may be used to determine the transition from minor tosignificant impact on production.

It will be appreciated that numerous specific details are set forth inorder to provide a thorough understanding of the example embodimentsdescribed herein. However, it will be understood by those of ordinaryskill in the art that the embodiments described herein may be practicedwithout these specific details. In other instances, well-known methods,procedures and components have not been described in detail so as not toobscure the embodiments described herein. Furthermore, this descriptionand the drawings are not to be considered as limiting the scope of theembodiments described herein in any way, but rather as merely describingthe implementation of the various embodiments described herein.

It should be noted that terms of degree such as “substantially”, “about”and “approximately” when used herein mean a reasonable amount ofdeviation of the modified term such that the end result is notsignificantly changed. These terms of degree should be construed asincluding a deviation of the modified term if this deviation would notnegate the meaning of the term it modifies.

In addition, as used herein, the wording “and/or” is intended torepresent an inclusive-or. That is, “X and/or Y” is intended to mean Xor Y or both, for example. As a further example, “X, Y, and/or Z” isintended to mean X or Y or Z or any combination thereof.

It should be noted that the term “coupled” used herein indicates thattwo elements can be directly coupled to one another or coupled to oneanother through one or more intermediate elements.

The embodiments of the systems and methods described herein may beimplemented in hardware or software, or a combination of both. Theseembodiments may be implemented in computer programs executing onprogrammable computers, each computer including at least one processor,a data storage system (including volatile memory or non-volatile memoryor other data storage elements or a combination thereof), and at leastone communication interface. For example and without limitation, theprogrammable computers (referred to below as computing devices) may be aserver, network appliance, embedded device, computer expansion module, apersonal computer, laptop, personal data assistant, cellular telephone,smart-phone device, tablet computer, a wireless device or any othercomputing device capable of being configured to carry out the methodsdescribed herein.

In some embodiments, the communication interface may be a networkcommunication interface. In embodiments in which elements are combined,the communication interface may be a software communication interface,such as those for inter-process communication (IPC). In still otherembodiments, there may be a combination of communication interfacesimplemented as hardware, software, and combination thereof.

Program code may be applied to input data to perform the functionsdescribed herein and to generate output information. The outputinformation is applied to one or more output devices, in known fashion.

Each program may be implemented in a high level procedural or objectoriented programming and/or scripting language, or both, to communicatewith a computer system. However, the programs may be implemented inassembly or machine language, if desired. In any case, the language maybe a compiled or interpreted language. Each such computer program may bestored on a storage media or a device (e.g. ROM, magnetic disk, opticaldisc) readable by a general or special purpose programmable computer,for configuring and operating the computer when the storage media ordevice is read by the computer to perform the procedures describedherein. Embodiments of the system may also be considered to beimplemented as a non-transitory computer-readable storage medium,configured with a computer program, where the storage medium soconfigured causes a computer to operate in a specific and predefinedmanner to perform the functions described herein.

Furthermore, the system, processes and methods of the describedembodiments are capable of being distributed in a computer programproduct comprising a computer readable medium that bears computer usableinstructions for one or more processors. The medium may be provided invarious forms, including one or more diskettes, compact disks, tapes,chips, wireline transmissions, satellite transmissions, internettransmission or downloadings, magnetic and electronic storage media,digital and analog signals, and the like. The computer useableinstructions may also be in various forms, including compiled andnon-compiled code.

Various embodiments have been described herein by way of example only.

Various modification and variations may be made to these exampleembodiments without departing from the spirit and scope of theinvention, which is limited only by the appended claims.

We claim:
 1. A method for optimizing a manufacturing assembly line, themanufacturing assembly line comprising a plurality of cells, each cellbeing configured to successively process a workpiece along themanufacturing assembly line, the method comprising operating a processorto: receive cell data associated with at least one cell of themanufacturing assembly line, the cell data comprising, for each cell, atleast one input state of that cell and a cell position of that cellwithin the manufacturing assembly line; extract feature data from thecell data, the feature data comprising at least one input state and atleast one cell position of at least one cell; determine a plurality ofcell configurations, each cell configuration corresponding to one celland defining a different process for processing the workpiece by thatone cell; determine an efficiency score for each cell configuration byapplying the extracted feature data to a predictive model generated forpredicting a production level of the manufacturing assembly line;determine at least one target cell configuration from the plurality ofcell configurations based on the efficiency score for each cellconfiguration; and apply the at least one target cell configuration toat least one cell by implementing each target cell configuration to acorresponding cell.
 2. The method of claim 1, wherein: determining theplurality of cell configurations comprises determining a plurality ofcell configurations for one cell; and determining the at least onetarget cell configuration comprises determining one target cellconfiguration for that one cell by selecting a cell configuration withthe highest efficiency score from the plurality of cell configurationsfor that one cell.
 3. The method of claim 1, wherein: determining theplurality of cell configurations comprises determining at least one cellconfiguration for each cell; and determining the at least one targetcell configuration comprises determining a target cell configuration foreach cell.
 4. The method of claim 3, wherein determining the target cellconfiguration for each cell comprises: determining a plurality of setsof cell configurations, each set of cell configurations comprising onecell configuration for each cell; determining an overall efficiencyscore for each set of cell configurations based on the efficiency scoreof each cell configuration in that set of cell configurations; andselecting a set of cell configurations with the highest overallefficiency score as the target cell configuration for each cell.
 5. Themethod of claim 1, wherein determining the efficiency score for eachcell configuration comprises: determining an input state of at least onecell upstream of a cell corresponding to that cell configuration; anddetermining the efficiency score for that cell configuration based onthe input state.
 6. The method of claim 1, wherein determining theefficiency score for each cell configuration comprises: determining aninput state of at least one cell downstream of a cell corresponding tothat cell configuration; and determining the efficiency score for thatcell configuration based on the input state.
 7. The method of claim 1,wherein determining the efficiency score for each cell configurationcomprises: determining a production level of the manufacturing assemblyline when that cell configuration is applied to a corresponding cell;and determining the efficiency score for that cell configuration basedon the production level.
 8. The method of claim 7, wherein determiningthe efficiency score for that cell configuration is based on whether theproduction level meets a predetermined production quota.
 9. The methodof claim 1, wherein determining the efficiency score for each cellconfiguration comprises: determining a defect level of the manufacturingassembly line when that cell configuration is applied to a correspondingcell; and determining the efficiency score for that cell configurationbased on the defect level.
 10. The method of claim 9, whereindetermining the efficiency score for that cell configuration comprises:determining at least one of a rework and a scrap cost based on thedefect level; and determining the efficiency score for that cellconfiguration based on the at least one of the rework and the scrapcost.
 11. The method of claim 9, wherein determining the efficiencyscore for that cell configuration is further based on whether the defectlevel meets a predetermined production quota.
 12. The method of claim 1,wherein: each cell comprises at least one devices configured to processthe workpiece; each cell configuration comprises at least one deviceconfiguration corresponding to a device of a corresponding cell anddefining a different process for processing the workpiece by thatdevice; applying the at least one target cell configuration comprisesimplementing each corresponding device configuration at a correspondingdevice.
 13. A system for optimizing a manufacturing assembly line, themanufacturing assembly line comprising a plurality of cells, each cellbeing configured to successively process a workpiece along themanufacturing assembly line, the system comprising a processorconfigured to: receive cell data associated with at least one cell ofthe manufacturing assembly line, the cell data comprising, for eachcell, at least one input state of that cell and a cell position of thatcell within the manufacturing assembly line; extract feature data fromthe cell data, the feature data comprising at least one input state andat least one cell position of at least one cell; determine a pluralityof cell configurations, each cell configuration corresponding to onecell and defining a different process for processing the workpiece bythat one cell; determine an efficiency score for each cell configurationby applying the extracted feature data to a predictive model generatedfor predicting a production level of the manufacturing assembly line;determine at least one target cell configuration from the plurality ofcell configurations based on the efficiency score for each cellconfiguration; and apply the at least one target cell configuration toat least one cell by implementing each target cell configuration to acorresponding cell.
 14. The system of claim 13, wherein the processor isfurther configured to: determine a plurality of cell configurations forone cell; and determine one target cell configuration for that one cellby selecting a cell configuration with the highest efficiency score fromthe plurality of cell configurations for that one cell.
 15. The systemof claim 13, wherein the processor is further configured to: determineat least one cell configuration for each cell; and determine a targetcell configuration for each cell.
 16. The system of claim 15, whereinthe processor is further configured to: determine a plurality of sets ofcell configurations, each set of cell configurations comprising one cellconfiguration for each cell; determine an overall efficiency score foreach set of cell configurations based on the efficiency score of eachcell configuration in that set of cell configurations; and select a setof cell configurations with the highest overall efficiency score as thetarget cell configuration for each cell.
 17. The system of claim 13,wherein the processor is further configured to: determine an input stateof at least one cell upstream of a cell corresponding to that cellconfiguration; and determine the efficiency score for that cellconfiguration based on the input state.
 18. The system of claim 13,wherein the processor is further configured to: determine an input stateof at least one cell downstream of a cell corresponding to that cellconfiguration; and determine the efficiency score for that cellconfiguration based on the input state.
 19. The system of claim 13,wherein the processor is further configured to: determine a productionlevel of the manufacturing assembly line when that cell configuration isapplied to a corresponding cell; and determine the efficiency score forthat cell configuration based on the production level.
 20. The system ofclaim 19, wherein the processor is configured to determine theefficiency score for that cell configuration based on whether theproduction level meets a predetermined production quota.
 21. The systemof claim 13, wherein the processor is configured to: determine a defectlevel of the manufacturing assembly line when that cell configuration isapplied to a corresponding cell; and determine the efficiency score forthat cell configuration based the defect level.
 22. The system of claim21, wherein the processor is configured to: determine at least one of arework and a scrap cost based on the defect level; and determine theefficiency score for that cell configuration based on the at least oneof the rework and the scrap cost.
 23. The system of claim 21, whereinthe processor is further configured to determine the efficiency scorefor that cell configuration based on whether the defect level meets apredetermined production quota.
 24. The system of claim 13, wherein:each cell comprises at least one devices configured to process theworkpiece; each cell configuration comprises at least one deviceconfiguration corresponding to a device of a corresponding cell anddefining a different process for processing the workpiece by thatdevice; the processor is configured to implement each correspondingdevice configuration at a corresponding device.